Orange Juice Historical Replay: 2020-06 to 2020-09
Replay 64 daily bars of orange juice from June 23 to September 22, 2020, navigating a 13.3% drawdown. You make 11 choices across volatility spikes, including 4 critical moments, then receive AI coach feedback on each decision in a simulated training environment.
64 daily bars · 11 decision points · 2020-06 to 2020-09 · Commodities
Key facts
This window spans 91 days with a peak-to-trough drawdown of 13.3% and a 8.6% run-up.
- Max drawdown
- −13.3%
- Max run-up
- +8.6%
- Duration
- 91 days
- Largest single-day move
- +6.2%
- Bars
- 64 Daily bars
- Decision points
- 11
Every figure above is computed programmatically from this replay’s own candle series — no editorial input, no LLM. Methodology: Methodology
The market advances day by day. You only see candles up to the current day — as if you were really there.
At key decision points choose Buy / Trim / Exit / Hold. Each decision advances the timeline by one day.
After settlement you get a six-dimension score and an AI coach review. Your result enters the scenario leaderboard.
Event Replay
What happened — and what it teaches
This replay places you inside the orange juice futures market during the summer of 2020, a period when soft commodities experienced unusual demand dynamics amid broader economic uncertainty. Spanning 64 daily bars from June 23 to September 22, 2020, the scenario captures a market environment tagged as a boom period yet marked by sharp intraday swings that tested trader discipline. What was at stake was not merely directional conviction but the ability to manage risk through environments where volatility itself became the primary challenge.
The price trajectory tells a nuanced story: starting at 119.85, the market ultimately closed at 112.9 for a net decline of -5.8%. Yet this headline number masks considerable turbulence beneath the surface. The maximum drawdown reached 13.3%, and the largest single-bar move measured 6.2%, underscoring how quickly positions could move against a trader. Across these 64 trading days, participants encountered 11 decision points, with 4 designated as critical moments requiring deliberate choices about position management and risk exposure.
For decision training, the scenario excels at isolating process over outcome. The drawdown sequence forces participants to confront the psychological challenge of holding through adverse moves while maintaining conviction in their analysis. The volatility spikes embedded within the data create realistic pressure points where pre-defined rules prove more valuable than reactive intuition. Four critical decision nodes serve as deliberate training checkpoints, each followed by AI coach feedback that unpacks the reasoning behind optimal responses. The lesson is clear: in volatile commodity markets, surviving the drawdown and maintaining decision quality through uncertainty defines long-term edge.
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